TLERAD: Transfer Learning for Enhanced Ransomware Attack Detection
Source abstract
Abstract
Ransomware has emerged as a critical cybersecurity threat, characterized by its ability to encrypt user data or lock devices, demanding ransom for their release. Traditional ransomware detection methods face limitations due t... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Malware Detection Techniques · Network Security and Intrusion Detection · Spam and Phishing Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (22)
Show source-provided references
1. A survey of crypto ransomware attack detection methodologies: An evolving outlook10.3390/s22051837 ↗
2. Ransomware: An interdisciplinary technical and legal approach10.1155/2022/2806605 ↗
3. Automated behavioral analysis of malware: A case study of wannacry ransomware
4. Machine learning in cybersecurity: A review of threat detection and defense mechanisms10.30574/wjarr.2024.21.1.0315 ↗
5. A review of machine learning-based zero-day attack detection: Challenges and future directions10.1016/j.comcom.2022.11.001 ↗
6. Dynamic feature dataset for ransomware detection using machine learning algorithms10.3390/s23031053 ↗
7. A review of cloud-based malware detection system: Opportunities, advances and challenges10.24018/ejeng.2021.6.3.2372 ↗
8. The age of ransomware: A survey on the evolution, taxonomy, and research directions10.1109/ACCESS.2023.3268535 ↗
9. Early detection of crypto-ransomware using pre-encryption detection algorithm
10. Reliable hardware architectures for cryptographic block ciphers LED and HIGHT10.1109/TCAD.2017.2661811 ↗
11. J. Kaur, A. Cintas Canto, M. Mozaffari Kermani, and R. Azarderakhsh, “A comprehensive survey on the implementations, attacks, and countermeasures of the current NIST lightweight cryptography standard,” Apr. 2023, arXiv2304.06222. doi: 10.1145/nnnnnnn.nnnnnnn.10.36227/techrxiv.22970855.v1 ↗
12. Post-quantum blockchain security for the Internet of Things: Survey and research directions10.1109/COMST.2024.3355222 ↗
13. Supersingular isogeny Diffie-Hellman key exchange on 64-bit ARM10.1109/TDSC.2017.2723891 ↗
14. An inception V3 approach for malware classification using machine learning and transfer learning
15. A survey on transfer learning10.1109/TKDE.2009.191 ↗
16. Enhancing ransomware attack detection using transfer learning and deep learning ensemble models on cloud-encrypted data10.3390/electronics12183899 ↗
17. RanSAP: An open dataset of ransomware storage access patterns for training machine learning models
18. On detection and prevention of zero-day attack using cuckoo sandbox in software-defined networks
19. Overcoming the lack of labeled data: Training malware detection models using adversarial domain adaptation
20. W. Gong, Y. Zha, and J. Tang, “Ransomware detection and classification using generative adversarial networks with dynamic weight adaptation,” Accessed: Aug. 12, 2024. [Online]. Available: https://files.osf.io/v1/resources/5vju7/providers/osfstorage/665069636b6c8e0da904cd11?action=download&direct&version=1
21. Cancer gene clustering using computational model
22. Explainability in AI-based behavioral malware detection systems10.1016/j.cose.2024.103842 ↗